UTILIZING MACHINE LEARNING TO DETERMINE THE COST OF MEDICAL INSURANCE

Abstract

By spreading the financial risk of unforeseen medical expenses among a large number of people, health insurance lowers the total amount of money at risk. Over the past 20 years, global public health spending has nearly doubled, and in 2023, it is predicted to reach $8.5 trillion, or 9.8% of the global GDP if inflation is taken into account. 60% of all medical procedures and 70% of outpatient care are provided by multinational multiprivate sectors, sometimes at exorbitant costs. Because of growing healthcare expenditures, longer life expectancies, and an increase in non-communicable diseases, health insurance has become a necessary good. The availability of insurance data has increased, allowing insurance companies to leverage predictive modeling to enhance their business operations and customer service. Computer algorithms and machine learning (ML) are used to analyze previous insurance data in order to estimate future output values based on consumer behavior patterns, insurance policies, data-driven decision-making, and the development of new schemes. Machine learning (ML) has shown a lot of potential in the insurance industry, which is why the ML Health Insurance Prediction System was developed. Medical expenditures can be reduced by using this cost-price prediction algorithm to estimate premium values more promptly and effectively. This system compares and contrasts the Random Forest Regressor, Support Vector Regression, and Linear Regression regression models. Because the models were trained on a dataset, predictions could be made and the model's effectiveness could be verified by comparing it to actual data.